626 citations · 913 across the 23 of their papers we have counts for
23 papers
Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Zhelun Shen, Xibin Song, Yuchao Dai +3
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and gene…
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
Jin Fang, Dingfu Zhou, Jingjing Zhao +4
Over the past few years, there has been remarkable progress in research on 3D point clouds and their use in autonomous driving scenarios has become widespread. However, deep learni…
Semi-supervised 3D Object Detection with Proficient Teachers
Junbo Yin, Jin Fang, Dingfu Zhou +4
Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples, however, 3D annotation in the point c…
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
Xuhong Li, Haoyi Xiong, Yi Liu +4
While fine-tuning pre-trained networks has become a popular way to train image segmentation models, such backbone networks for image segmentation are frequently pre-trained using i…
ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
Junbo Yin, Dingfu Zhou, Liangjun Zhang +4
Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose l…
A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +3
3D point cloud registration in remote sensing field has been greatly advanced by deep learning based methods, where the rigid transformation is either directly regressed from the t…
End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +3
Even though considerable progress has been made in deep learning-based 3D point cloud processing, how to obtain accurate correspondences for robust registration remains a major cha…
AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection
Zongdai Liu, Dingfu Zhou, Feixiang Lu +2
Existing deep learning-based approaches for monocular 3D object detection in autonomous driving often model the object as a rotated 3D cuboid while the object's geometric shape has…
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
Shaoqing Xu, Dingfu Zhou, Jin Fang +3
Accurate detection of obstacles in 3D is an essential task for autonomous driving and intelligent transportation. In this work, we propose a general multimodal fusion framework Fus…
MapFusion: A General Framework for 3D Object Detection with HDMaps
Jin Fang, Dingfu Zhou, Xibin Song +1
3D object detection is a key perception component in autonomous driving. Most recent approaches are based on Lidar sensors only or fused with cameras. Maps (e.g., High Definition M…
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image
Dingfu Zhou, Xibin Song, Yuchao Dai +5
3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed. However, the task is intrinsically ambiguous a…
DVI: Depth Guided Video Inpainting for Autonomous Driving
Miao Liao, Feixiang Lu, Dingfu Zhou +3
To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and syn…
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving
Feixiang Lu, Zongdai Liu, Xibin Song +8
We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving. Our approach combines the streng…
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Xibin Song, Yuchao Dai, Dingfu Zhou +4
Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a maj…
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
Zhelun Shen, Yuchao Dai, Xibin Song +3
Existing deep learning based stereo matching methods either focus on achieving optimal performances on the target dataset while with poor generalization for other datasets or focus…
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
Junbo Yin, Jianbing Shen, Chenye Guan +2
Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper…
Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem
Liu Liu, Dylan Campbell, Hongdong Li +3
Conventional absolute camera pose via a Perspective-n-Point (PnP) solver often assumes that the correspondences between 2D image pixels and 3D points are given. When the correspond…
IoU Loss for 2D/3D Object Detection
Dingfu Zhou, Jin Fang, Xibin Song +4
In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing sta…
Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry
Dingfu Zhou, Yuchao Dai, Hongdong Li
Recovering the absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems. By using different kinds of cues, var…
ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving
Xibin Song, Peng Wang, Dingfu Zhou +6
Autonomous driving has attracted remarkable attention from both industry and academia. An important task is to estimate 3D properties(e.g.translation, rotation and shape) of a movi…
Augmented LiDAR Simulator for Autonomous Driving
Jin Fang, Dingfu Zhou, Feilong Yan +5
In Autonomous Driving (AD), detection and tracking of obstacles on the roads is a critical task. Deep-learning based methods using annotated LiDAR data have been the most widely ad…
The ApolloScape Open Dataset for Autonomous Driving and its Application
Xinyu Huang, Peng Wang, Xinjing Cheng +3
Autonomous driving has attracted tremendous attention especially in the past few years. The key techniques for a self-driving car include solving tasks like 3D map construction, se…
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion
Dingfu Zhou, Yuchao Dai, Hongdong Li
Large field-of-view fisheye lens cameras have attracted more and more researchers' attention in the field of robotics. However, there does not exist a convenient off-the-shelf ster…